merge commits renewing forms from branch v0.4.0 to main (#800)

* zht: renew README forms. (#792) * chore: renew README forms (#791) Co-authored-by: ChongWei905 <weichong4@huawei.com> --------- Co-authored-by: JingRiGuMing <48117115+JingRiGuMing@users.noreply.github.com> Co-authored-by: ChongWei905 <weichong4@huawei.com>
mindspore-lab · Jul 25, 2024 · 6e2a4c7 · 6e2a4c7
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diff --git a/configs/bit/README.md b/configs/bit/README.md
@@ -17,13 +17,19 @@ too low. 5) With BiT fine-tuning, good performance can be achieved even if there
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
+*coming soon*
+
+performance tested on ascend 910(8p) with graph mode
+
+
 <div align="center">
 
-| Model          | Context  | Top-1 (%) | Top-5 (%) | Params(M) | Recipe                                                                                           |                                    Download                                 |
-|----------------| -------- |-----------|-----------|-----------|--------------------------------------------------------------------------------------------------|  -------------------------------------------------------------------------- |
-| bit_resnet50   | D910x8-G | 76.81     | 93.17     | 25.55     | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml)   | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
-| bit_resnet50x3 | D910x8-G | 80.63     | 95.12     | 217.31    | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50x3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50x3-a960f91f.ckpt) |
-| bit_resnet101  | D910x8-G | 77.93     | 93.75     | 44.54     | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet101_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet101-2efa9106.ckpt) |
+| Model        | Top-1 (%) | Top-5 (%) | Params(M) | Batch Size | Recipe                                                                                         | Download                                                                                |
+| ------------ | --------- | --------- | --------- | ---------- | ---------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
+| bit_resnet50 | 76.81     | 93.17     | 25.55     | 32         | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/bit/bit_resnet50_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/bit/BiT_resnet50-1e4795a4.ckpt) |
+
 
 </div>
 

diff --git a/configs/cmt/README.md b/configs/cmt/README.md
@@ -14,11 +14,17 @@ on ImageNet-1K dataset.
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
+*coming soon*
+
+performance tested on ascend 910(8p) with graph mode
+
 <div align="center">
 
-| Model     | Context  | Top-1 (%) | Top-5 (%) | Params(M) | Recipe                                                                                      | Download                                                                             |
-|-----------| -------- |-----------|-----------|-----------|---------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
-| cmt_small | D910x8-G | 83.24     | 96.41     | 26.09     | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
+| Model     | Top-1 (%) | Top-5 (%) | Params(M) | Batch Size | Recipe                                                                                      | Download                                                                             |
+| --------- | --------- | --------- | --------- | ---------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
+| cmt_small | 83.24     | 96.41     | 26.09     | 128        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.ckpt) |
 
 
 </div>

diff --git a/configs/coat/README.md b/configs/coat/README.md
@@ -10,14 +10,18 @@ Co-Scale Conv-Attentional Image Transformer (CoaT) is a Transformer-based image
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
+*coming soon*
+
+
+performance tested on ascend 910(8p) with graph mode
+
 <div align="center">
 
-| Model           | Context   |  Top-1 (%)  |  Top-5 (%)  | Params (M) | Recipe                                                                                   | Weight                                                                            |
-|-----------------|-----------|-------|------------|------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------|----------------------------------------------------------------------------------|
-| coat_lite_tiny | D910x8-G | 77.35 | 93.43 | 5.72      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_lite_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_lite_tiny-fa7bf894.ckpt) |
-| coat_lite_mini | D910x8-G | 78.51 | 93.84 | 11.01 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_lite_mini_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_lite_mini-55a52f05.ckpt) |
-| coat_tiny | D910x8-G | 79.67 | 94.88 | 5.50 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
-| coat_mini | D910x8-G | 81.08 | 95.34 | 10.34 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_mini_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_mini-57c5bce7.ckpt) |
+| Model     | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                       | Weight                                                                                |
+| --------- | --------- | --------- | ---------- | ---------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
+| coat_tiny | 79.67     | 94.88     | 5.50       | 32         | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/coat/coat_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/coat/coat_tiny-071cb792.ckpt) |
 
 </div>
 

diff --git a/configs/convit/README.md b/configs/convit/README.md
@@ -24,16 +24,24 @@ while offering a much improved sample efficiency.[[1](#references)]
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
+
+<div align="center">
+
+| Model       | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                           | Download    |
+| ----------- | --------- | --------- | ---------- | ---------- | ------------------------------------------------------------------------------------------------ |-------------|
+| convit_tiny | 73.79     | 91.70     | 5.71       | 256        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convit/convit_tiny-1961717e-910v2.ckpt) |
+
+</div>
+
+performance tested on ascend 910(8p) with graph mode
+
 <div align="center">
 
-| Model             | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                                 | Download                                                                               |
-|-------------------|----------|-----------|-----------|------------|--------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
-| convit_tiny       | D910x8-G | 73.66     | 91.72     | 5.71       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml)       | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt)       |
-| convit_tiny_plus  | D910x8-G | 77.00     | 93.60     | 9.97       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_plus_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny_plus-e9d7fb92.ckpt)  |
-| convit_small      | D910x8-G | 81.63     | 95.59     | 27.78      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_small_ascend.yaml)      | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_small-ba858604.ckpt)      |
-| convit_small_plus | D910x8-G | 81.80     | 95.42     | 48.98      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_small_plus_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_small_plus-2352b9f7.ckpt) |
-| convit_base       | D910x8-G | 82.10     | 95.52     | 86.54      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_base_ascend.yaml)       | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_base-c61b808c.ckpt)       |
-| convit_base_plus  | D910x8-G | 81.96     | 95.04     | 153.13     | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_base_plus_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_base_plus-5c61c9ce.ckpt)  |
+| Model       | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                           | Download                                                                                  |
+| ----------- | --------- | --------- | ---------- | ---------- | ------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- |
+| convit_tiny | 73.66     | 91.72     | 5.71       | 256        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convit/convit_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convit/convit_tiny-e31023f2.ckpt) |
 
 </div>
 

diff --git a/configs/convnext/README.md b/configs/convnext/README.md
@@ -21,13 +21,25 @@ simplicity and efficiency of standard ConvNets.[[1](#references)]
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
 <div align="center">
 
-| Model          | Context   | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                                | Download                                                                                       |
-|----------------|-----------|-----------|-----------|------------|-------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
-| convnext_tiny  | D910x64-G | 81.91     | 95.79     | 28.59      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt)  |
-| convnext_small | D910x64-G | 83.40     | 96.36     | 50.22      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt) |
-| convnext_base  | D910x64-G | 83.32     | 96.24     | 88.59      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_base_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.ckpt)  |
+| Model         | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                               | Download    |
+| ------------- | --------- | --------- | ---------- | ---------- | ---------------------------------------------------------------------------------------------------- |-------------|
+| convnext_tiny | 81.28     | 95.61     | 28.59      | 16         | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnext/convnext_tiny-db11dc82-910v2.ckpt) |
+
+
+</div>
+
+performance tested on ascend 910(8p) with graph mode
+
+<div align="center">
+
+| Model         | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                               | Download                                                                                      |
+| ------------- | --------- | --------- | ---------- | ---------- | ---------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- |
+| convnext_tiny | 81.91     | 95.79     | 28.59      | 16         | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt) |
+
 
 </div>
 

diff --git a/configs/convnextv2/README.md b/configs/convnextv2/README.md
@@ -20,11 +20,23 @@ benchmarks, including ImageNet classification, COCO detection, and ADE20K segmen
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
+<div align="center">
+
+| Model           | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                                   | Download    |
+| --------------- | --------- | --------- | ---------- | ---------- | -------------------------------------------------------------------------------------------------------- |-------------|
+| convnextv2_tiny | 82.39     | 95.95     | 28.64      | 128        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-a35b79ce-910v2.ckpt) |
+
+</div>
+
+performance tested on ascend 910(8p) with graph mode
+
 <div align="center">
 
-| Model            | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                                   | Download                                                                                          |
-|------------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
-| convnextv2_tiny | D910x8-G | 82.43     | 95.98     | 28.64      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
+| Model           | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                                   | Download                                                                                          |
+| --------------- | --------- | --------- | ---------- | ---------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- |
+| convnextv2_tiny | 82.43     | 95.98     | 28.64      | 128        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
 
 </div>
 

diff --git a/configs/crossvit/README.md b/configs/crossvit/README.md
@@ -19,14 +19,23 @@ Fusion is achieved by an efficient cross-attention module, in which each transfo
 
 Our reproduced model performance on ImageNet-1K is reported as follows.
 
+performance tested on ascend 910*(8p) with graph mode
+
 <div align="center">
 
-| Model       | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                  | Download                                                                              |
-|-------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|
-| crossvit_9 | D910x8-G | 73.56     | 91.79     | 8.55      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt)     |
-| crossvit_15 | D910x8-G | 81.08     | 95.33     | 27.27      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_15_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_15-eaa43c02.ckpt)     |
-| crossvit_18 | D910x8-G | 81.93     | 95.75     | 43.27      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_18_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_18-ca0a2e43.ckpt) |
+| Model      | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                            | Download    |
+| ---------- | --------- | --------- | ---------- | ---------- | ------------------------------------------------------------------------------------------------- |-------------|
+| crossvit_9 | 73.38     | 91.51     | 8.55       | 256        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download-mindspore.osinfra.cn/toolkits/mindcv/crossvit/crossvit_9-32c69c96-910v2.ckpt) |
+
+</div>
+
+performance tested on ascend 910(8p) with graph mode
+
+<div align="center">
 
+| Model      | Top-1 (%) | Top-5 (%) | Params (M) | Batch Size | Recipe                                                                                            | Download                                                                                   |
+| ---------- | --------- | --------- | ---------- | ---------- | ------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ |
+| crossvit_9 | 73.56     | 91.79     | 8.55       | 256        | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/crossvit/crossvit_9_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/crossvit/crossvit_9-e74c8e18.ckpt) |
 
 </div>